Strategic Project Lead, RL

Strategic Project Lead

$180k-$220k base founding employee equity

New York City preferred (flexible depending on situation)

Morpheus are partnered with a well-funded, commercially live startup building the evaluation and training infrastructure for autonomous healthcare AI. They create high-fidelity RL training gyms and environments that improve frontier models and agents on real clinical, operational, and administrative workflows, using long-horizon evaluations, expert feedback, verifier-driven tasks, RL environment design, and real-world healthcare data. They're backed by premier VCs, already connected with frontier labs, and hiring to meet immediate delivery needs.

They're looking for an exceptional Founding Strategic Project Lead to own lab-facing delivery and expansion across their existing frontier lab relationships, turning early engagements into shipped RL environments, gym artefacts, and post-training datasets that convert into larger contracts. This role suits someone with extremely high agency who has shipped complex technical projects directly to frontier labs, understands RL environment design and post-training data pipelines, and can operate under ambiguity in a small, fast-moving startup while holding a high quality bar.

What you'll be doing:

• Owning end-to-end execution across lab engagements, including scoping, timelines, milestones, risks, and delivery of RL environments and post-training data artefacts
• Acting as the customer-facing point of contact with frontier lab stakeholders, running cadence, aligning on RL environment specs and acceptance criteria, and communicating progress clearly
• Translating ambiguous lab asks into concrete workstreams, including RL environment/gym spec, reward and verifier design, task library plan, QC plan, and delivery roadmap
• Driving "quick-hit" pilots, delivering small, high-quality sample RL environments or post-training data slices early to prove fit and de-risk full delivery
• Coordinating cross-functionally across ML/RL researchers, environment engineers, and clinicians/domain experts to ship complex, multi-step projects on time and at frontier-lab quality
• Building and enforcing delivery quality systems for RL environments and datasets, including definition-of-done checklists, verifier/reward QC gates, auditability, versioning, and postmortems
• Helping convert successful deliveries into expanded scopes and longer-term contracts with frontier labs

What they're looking for:

• Demonstrated experience delivering complex technical projects directly to frontier AI labs, as a vendor, partner, or embedded team, ideally involving RL environments, evals, or post-training deliverables
• A working understanding of RL environments, including reward design, verifiers, environment/task construction, and how these feed into model training and evaluation
• Hands-on familiarity with post-training data pipelines, including data/annotation workflows and RLHF/RLAIF-style processes
• An exceptional ability to turn ambiguity into structure, with clear goals, tight scope, crisp milestones, and measurable success criteria on multi-workstream projects
• Strong written communication, able to produce specs, updates, and summaries that reduce confusion and accelerate decisions with technical stakeholders
• High agency and comfort operating in a small startup with limited process, resources, or precedent to lean on
• The ability to work with technical teams and domain experts while making high-judgement tradeoffs on scope, quality, and timelines

Nice to have:

• Experience in regulated domains such as healthcare, fintech, or security, where QA/QC and auditability matter
• Experience converting early customer demand into expanded contracts

This is a chance to sit at the centre of one of the most important bottlenecks in AI: making autonomous agents reliable in real-world, high-stakes domains through better RL environments and post-training data.